{"id":"https://openalex.org/W4387346536","doi":"https://doi.org/10.1145/3584371.3613067","title":"Physics-Guided Deep Generative Model For New Ligand Discovery","display_name":"Physics-Guided Deep Generative Model For New Ligand Discovery","publication_year":2023,"publication_date":"2023-09-03","ids":{"openalex":"https://openalex.org/W4387346536","doi":"https://doi.org/10.1145/3584371.3613067","pmid":"https://pubmed.ncbi.nlm.nih.gov/38706556"},"language":"en","primary_location":{"id":"doi:10.1145/3584371.3613067","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584371.3613067","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584371.3613067","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3584371.3613067","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026121268","display_name":"Dikshant Sagar","orcid":"https://orcid.org/0000-0003-1788-6410"},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dikshant Sagar","raw_affiliation_strings":["Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States"],"raw_orcid":"https://orcid.org/0000-0003-1788-6410","affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States","institution_ids":["https://openalex.org/I27825529"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082696725","display_name":"Ali Risheh","orcid":"https://orcid.org/0009-0007-6548-9205"},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Risheh","raw_affiliation_strings":["Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States"],"raw_orcid":"https://orcid.org/0009-0007-6548-9205","affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States","institution_ids":["https://openalex.org/I27825529"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042406036","display_name":"N.M. Sheikh","orcid":"https://orcid.org/0009-0000-7788-9707"},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nida Sheikh","raw_affiliation_strings":["Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States"],"raw_orcid":"https://orcid.org/0009-0000-7788-9707","affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States","institution_ids":["https://openalex.org/I27825529"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081213503","display_name":"Negin Forouzesh","orcid":"https://orcid.org/0000-0003-2293-0391"},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Negin Forouzesh","raw_affiliation_strings":["Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States"],"raw_orcid":"https://orcid.org/0000-0003-2293-0391","affiliations":[{"raw_affiliation_string":"Department of Computer Science, California State University, Los Angeles, Los Angeles, California, United States","institution_ids":["https://openalex.org/I27825529"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27825529"],"apc_list":null,"apc_paid":null,"fwci":2.3491,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.90197909,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2023","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.6437305212020874},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5825251340866089},{"id":"https://openalex.org/keywords/ligand","display_name":"Ligand (biochemistry)","score":0.5432544350624084},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5294373035430908},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.46891888976097107},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4645084738731384},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4297420382499695},{"id":"https://openalex.org/keywords/small-molecule","display_name":"Small molecule","score":0.4227394461631775},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.39549142122268677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3850483298301697},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.3632102608680725},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3139018416404724},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.2452639937400818},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.2408478558063507},{"id":"https://openalex.org/keywords/receptor","display_name":"Receptor","score":0.10584813356399536}],"concepts":[{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.6437305212020874},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5825251340866089},{"id":"https://openalex.org/C116569031","wikidata":"https://www.wikidata.org/wiki/Q899107","display_name":"Ligand (biochemistry)","level":3,"score":0.5432544350624084},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5294373035430908},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46891888976097107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4645084738731384},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4297420382499695},{"id":"https://openalex.org/C161624437","wikidata":"https://www.wikidata.org/wiki/Q1988322","display_name":"Small molecule","level":2,"score":0.4227394461631775},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.39549142122268677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3850483298301697},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3632102608680725},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3139018416404724},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.2452639937400818},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.2408478558063507},{"id":"https://openalex.org/C170493617","wikidata":"https://www.wikidata.org/wiki/Q208467","display_name":"Receptor","level":2,"score":0.10584813356399536},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3584371.3613067","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584371.3613067","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584371.3613067","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},{"id":"pmid:38706556","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38706556","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine","raw_type":null},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/116606","is_oa":true,"landing_page_url":"http://hdl.handle.net/10919/116606","pdf_url":"https://vtechworks.lib.vt.edu/bitstreams/405d5789-ba8f-474d-9c5e-081acf872161/download","source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article - Refereed"},{"id":"pmh:oai:pubmedcentral.nih.gov:11067829","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11067829","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11067829/pdf/nihms-1987954.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM BCB","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3584371.3613067","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584371.3613067","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584371.3613067","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5}],"awards":[{"id":"https://openalex.org/G4060228300","display_name":"Improving the Accuracy of Implicit Solvents with a Physics-Guided Neural Network","funder_award_id":"5r16gm146633-02","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G481293711","display_name":null,"funder_award_id":"R16 GM146633","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G8063788348","display_name":"PREC Track 1: Cal State LA - MolSSI PREC Pathway to Diversity Program","funder_award_id":"2216858","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387346536.pdf","grobid_xml":"https://content.openalex.org/works/W4387346536.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1594483434","https://openalex.org/W1973060079","https://openalex.org/W1975147762","https://openalex.org/W2092285329","https://openalex.org/W2117620409","https://openalex.org/W2169678694","https://openalex.org/W2170773610","https://openalex.org/W2194775991","https://openalex.org/W2529996553","https://openalex.org/W2578240541","https://openalex.org/W2583907533","https://openalex.org/W2610148085","https://openalex.org/W2742305903","https://openalex.org/W2952635193","https://openalex.org/W2969980075","https://openalex.org/W3006477052","https://openalex.org/W3028831932","https://openalex.org/W3098269892","https://openalex.org/W3104705366","https://openalex.org/W3168430821","https://openalex.org/W3209764902","https://openalex.org/W4230950854","https://openalex.org/W4283729753","https://openalex.org/W4289436753","https://openalex.org/W4294017990"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W2043558654","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W2967848559","https://openalex.org/W4299831724"],"abstract_inverted_index":{"Structure-based":[0],"drug":[1],"discovery":[2],"aims":[3],"to":[4,11,44],"identify":[5],"small":[6,110],"molecules":[7,30],"that":[8,87],"can":[9],"attach":[10],"a":[12,66,93,96],"specific":[13,32],"target":[14],"protein":[15],"and":[16,35,56,95,109,174,184,194,207],"change":[17],"its":[18],"functionality.":[19],"Recently,":[20],"deep":[21,68,177,210],"learning":[22,178,211],"has":[23,102],"shown":[24],"great":[25],"promise":[26],"in":[27,58],"generating":[28],"drug-like":[29],"with":[31,37],"biochemical":[33],"features":[34,86],"conditioned":[36,75],"structural":[38],"features.":[39],"However,":[40],"they":[41],"usually":[42],"fail":[43],"incorporate":[45],"an":[46,156],"essential":[47],"factor:":[48],"the":[49,79,89,114,123,134,147,151,165,170,175,187,196,209],"underlying":[50],"physics":[51],"which":[52],"guides":[53],"molecular":[54],"formation":[55],"binding":[57,80,90],"real-world":[59],"scenarios.":[60],"In":[61],"this":[62],"work,":[63],"we":[64],"describe":[65,88],"physics-guided":[67,172],"generative":[69],"model":[70,101,129,173,179,198],"for":[71],"new":[72],"ligand":[73],"discovery,":[74],"not":[76],"only":[77],"on":[78,84,105,117,199],"site":[81],"but":[82],"also":[83],"physics-based":[85,205],"mechanism":[91],"between":[92],"receptor":[94],"ligand.":[97,137],"The":[98,162,189],"proposed":[99,171],"hybrid":[100,128,197],"been":[103],"tested":[104],"large":[106],"protein-ligand":[107],"complexes":[108],"host-guest":[111],"systems.":[112],"Using":[113],"top-N":[115],"methodology,":[116],"average":[118,157],"more":[119,181,203],"than":[120,133,146],"75%":[121],"of":[122,139,159,164],"generated":[124,149,168],"structures":[125],"by":[126,150,155,169,186],"our":[127],"were":[130],"stronger":[131],"binders":[132],"original":[135],"reference":[136,176],"All":[138],"them":[140],"had":[141],"higher":[142],"\u0394Gbind":[143],"(affinity)":[144],"values":[145],"ones":[148],"previous":[152],"state-of-the-art":[153],"method":[154],"margin":[158],"1.88":[160],"kcal/mol.":[161],"visualization":[163],"top-5":[166],"ligands":[167],"demonstrate":[180],"feasible":[182],"conformations":[183],"orientations":[185],"former.":[188],"future":[190],"directions":[191],"include":[192],"training":[193],"testing":[195],"larger":[200],"datasets,":[201],"adding":[202],"relevant":[204],"features,":[206],"interpreting":[208],"outcomes":[212],"from":[213],"biophysical":[214],"perspectives.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}